def setup_class(cls):
path = os.path.join(TEST_PATH, "de21_no-heat_csv")
sc = st.DeflexScenario()
sc.read_csv(path)
cls.tables = sc.input_data
tmp_tables = {}
name = "heat_demand_deflex"
fn = os.path.join(os.path.dirname(__file__), "data", name + ".csv")
tmp_tables[name] = pd.read_csv(fn, index_col=[0], header=[0, 1])
name = "transformer_balance"
fn = os.path.join(os.path.dirname(__file__), "data", name + ".csv")
tmp_tables[name] = pd.read_csv(fn, index_col=[0, 1, 2], header=[0])
powerplants.scenario_powerplants = MagicMock(
return_value={
"volatile plants": cls.tables["volatile plants"],
"power plants": cls.tables["power plants"],
})
feedin.scenario_feedin = MagicMock(
return_value=cls.tables["volatile series"])
demand_table = {
"electricity demand series":
cls.tables["electricity demand series"]
}
demand.scenario_demand = MagicMock(return_value=demand_table)
my_parameter = {
"copperplate": True,
"group_transformer": False,
"heat": False,
"use_variable_costs": True,
"use_CO2_costs": True,
"map": "de21",
}
my_name = "deflex"
for k, v in my_parameter.items():
my_name += "_" + str(k) + "-" + str(v)
polygons = deflex_regions(rmap=my_parameter["map"], rtype="polygons")
lines = deflex_power_lines(my_parameter["map"]).index
base = os.path.join(os.path.expanduser("~"), ".tmp_x345234dE_deflex")
os.makedirs(base, exist_ok=True)
path = os.path.join(base, "deflex_test{0}")
name = "deflex_2014_de21_no-heat"
scenario_creator.create_basic_reegis_scenario(
name=name,
regions=polygons,
lines=lines,
parameter=my_parameter,
excel_path=path.format(".xlsx"),
csv_path=path.format("_csv"),
)
sc_new = st.DeflexScenario()
sc_new.read_csv(path.format("_csv"))
cls.input_data = sc_new.input_data
def test_scenario_es_init():
data = {
"general":
pd.Series({
"year": 2013,
"name": "test",
"number of time steps": 8760
})
}
sc = scenario.DeflexScenario(input_data=data)
es1 = sc.initialise_energy_system().es
sc = scenario.DeflexScenario(input_data=data)
sc.input_data["general"]["year"] = 2012
with pytest.warns(UserWarning, match="2012 is a leap year but the"):
print(sc.initialise_energy_system().es)
sc.input_data["general"]["number of time steps"] = 8784
es2 = sc.initialise_energy_system().es
assert len(es1.timeindex) == 8760
assert len(es2.timeindex) == 8784
def test_scenario_building():
sc = scenario.DeflexScenario(name="test", year=2014)
csv_path = os.path.join(TEST_PATH, "de22_heat_transmission_csv")
sc.read_csv(csv_path)
sc.table2es()
sc.check_input_data()
sc.input_data["volatile series"].loc[5, ("DE01", "wind")] = float("nan")
with pytest.raises(ValueError,
match=r"NaN values found in table:'volatile series'"):
sc.check_input_data()
def test_corrupt_data():
sc = scenario.DeflexScenario(year=2014)
csv_path = os.path.join(TEST_PATH, "de03_fictive_csv")
sc.read_csv(csv_path)
sc.input_data["volatile series"].drop(("DE02", "solar"),
inplace=True,
axis=1)
msg = "Missing time series for solar"
with pytest.raises(ValueError, match=msg):
sc.table2es()
def test_excel_reader():
sc = scenario.DeflexScenario()
xls_fn = fetch_test_files("de02_heat.xlsx")
sc.read_xlsx(xls_fn)
sc.initialise_energy_system()
sc.table2es()
csv_path = os.path.join(TEST_PATH, "deflex_2013_de02_tmp_X45_test_csv")
sc.to_csv(csv_path)
xls_fn = os.path.join(TEST_PATH, "deflex_2014_de02_tmp_X45_test")
sc.to_xlsx(xls_fn)
xls_fn += ".xlsx"
sc.to_xlsx(xls_fn)
shutil.rmtree(csv_path)
os.remove(xls_fn)
def test_build_model_manually():
sc = scenario.DeflexScenario(debug=True)
xls_fn = fetch_test_files("de02_no-heat.xlsx")
sc.read_xlsx(xls_fn)
sc.initialise_energy_system()
test_nodes = sc.create_nodes()
sc.add_nodes_to_es(test_nodes)
dump_fn = os.path.join(TEST_PATH, "pytest_test")
sc.dump(dump_fn)
model = sc.create_model()
sc.solve(model=model, solver="cbc", with_duals=True)
assert (
sc.es.results["meta"]["name"] == "deflex_2014_de02_no-heat_reg-merit")
dump_fn += ".dflx"
sc.dump(dump_fn)
plot_fn = os.path.join(TEST_PATH, "test_plot_X343.graphml")
sc.plot_nodes(plot_fn)
assert os.path.isfile(plot_fn)
scenario.restore_scenario(dump_fn)
assert int(sc.meta["year"]) == 2014
os.remove(dump_fn)
os.remove(plot_fn)
# the xlsx scenarios are the same. The csv-directories cen be read faster by
# the computer but the xlsx-files are easier to read for humans because all
# sheets are in one file.
# NOTE: Large models will need up to 24 GB of RAM, so start with small models
# and increase the size step by step. You can also use large models with less
# time steps but you have to adapt the annual limits.
# Now choose one example. We will start with a small one:
file = "deflex_2014_de02_no-heat_csv"
fn = os.path.join(path, file)
# *** Long version ***
# Create a scenario object
sc = scenario.DeflexScenario()
# Read the input data. Use the right method (csv/xlsx) for your file type.
sc.read_csv(fn)
# sc.read_xlsx(fn)
# Create the LP model and solve it.
sc.compute()
# Dump the results to a sub-dir named "results_cbc".
dump_file = file.replace("_csv", ".dflx")
# dump_file = file.replace(".xlsx")
dump_path = os.path.join(path, "results_cbc", dump_file)
sc.dump(dump_path)
# *** short version ***
def setup_class(cls):
path = os.path.join(TEST_PATH, "de22_heat_transmission_csv")
sc = st.DeflexScenario()
sc.read_csv(path)
cls.tables = sc.input_data
tmp_tables = {}
parameter = {
"costs_source": "ewi",
"downtime_bioenergy": 0.1,
"limited_transformer": "bioenergy",
"local_fuels": "district heating",
"map": "de22",
"mobility_other": "petrol",
"round": 1,
"separate_heat_regions": "de22",
"copperplate": False,
"default_transmission_efficiency": 0.9,
"group_transformer": False,
"heat": True,
"use_CO2_costs": True,
"use_downtime_factor": True,
"use_variable_costs": False,
"year": 2014,
}
config.init(paths=[os.path.dirname(dfile)])
for option, value in parameter.items():
cfg.tmp_set("creator", option, str(value))
config.tmp_set("creator", option, str(value))
name = "heat_demand_deflex"
fn = os.path.join(os.path.dirname(__file__), "data", name + ".csv")
tmp_tables[name] = pd.read_csv(fn, index_col=[0], header=[0, 1])
name = "transformer_balance"
fn = os.path.join(os.path.dirname(__file__), "data", name + ".csv")
tmp_tables[name] = pd.read_csv(fn, index_col=[0, 1, 2], header=[0])
powerplants.scenario_powerplants = MagicMock(
return_value={
"volatile plants": cls.tables["volatile plants"],
"power plants": cls.tables["power plants"],
})
powerplants.scenario_chp = MagicMock(
return_value={
"heat-chp plants": cls.tables["heat-chp plants"],
"power plants": cls.tables["power plants"],
})
feedin.scenario_feedin = MagicMock(
return_value=cls.tables["volatile series"])
demand_table = {
"electricity demand series":
cls.tables["electricity demand series"],
"heat demand series": cls.tables["heat demand series"],
}
demand.scenario_demand = MagicMock(return_value=demand_table)
name = "deflex_2014_de22_heat_transmission"
polygons = deflex_regions(rmap=parameter["map"], rtype="polygons")
lines = deflex_power_lines(parameter["map"]).index
cls.input_data = scenario_creator.create_scenario(
polygons, 2014, name, lines)
def test_build_model():
sc = scenario.DeflexScenario(debug=True)
xls_fn = fetch_test_files("de02_heat.xlsx")
sc.read_xlsx(xls_fn)
sc.compute()
assert sc.es.results["meta"]["name"] == "deflex_2014_de02_heat_reg-merit"
def test_scenario_es_init_error():
sc = scenario.DeflexScenario()
msg = "There is no input data in the scenario. You cannot initialise an"
with pytest.raises(ValueError, match=msg):
sc.initialise_energy_system()
def setup_class(cls):
cls.sc = scenario.DeflexScenario()
fn = os.path.join(TEST_PATH, "de02_no-heat.xlsx")
cls.sc.read_xlsx(fn)
cls.sc.input_data["general"]["regions"] = float("nan")